Book Exploration Days

Forward-deployed data & AI engineering · Amsterdam

Forward-deployed data & AI engineering.
One workflow at a time, into production.

We embed senior engineers with your team and take one operational workflow from your data platform — Databricks or Microsoft Fabric, on Azure, AWS or GCP — to a production AI system in weeks, with whichever model wins the eval, and hand it over running.

Book Exploration DaysHow a deployment runs
  • Based in Amsterdam
  • Databricks + Microsoft Fabric
  • Azure · AWS · GCP
  • Model-neutral

Every deployment, end to end

Your systems stay where they are. Nothing is replaced.

  1. Systems of recordSAP · Salesforce · core systems · documents
  2. Lakehouse + Unity CatalogDatabricks or Fabric, governed
  3. Business semanticsThe workflow's own vocabulary
  4. Agents + modelsChosen by eval, not by partnership
  5. Workflow appHuman approval, audit trail
  6. The people who run itIn their own tool, every morning
  7. A measured outcomeAgainst the baseline we captured

What forward-deployed means

Not a strategy deck. Not a migration. Not engineers by the hour.

Our engineers sit with the people who run the workflow, connect the data you already have, build the system, put it into production, and leave you running it.

The pod is senior only. The customer names the domain owner of the workflow before we start — no owner, no start.

What we build

Three workflows we take to production.

  • Document-to-decision

    Invoices, claims, KYC files, contracts, compliance filings. Extraction into governed tables, an agent that assembles the case and proposes the decision, human approval with a full audit trail.

    KPI · Hours per document · Cycle time · Straight-through rate

  • Cross-system exception resolution

    Failed orders, shipments, work orders, payments. Exceptions land in the lakehouse; an agent triages, pulls context from ERP, WMS and CRM, proposes a resolution, and routes anything outside policy to a person.

    KPI · Mean time to resolve · Auto-resolved within policy · Backlog

  • Governed knowledge workflow

    For a support, field-service or compliance team. A governed knowledge layer with access control, an assistant inside the team's own tool that answers, drafts and executes bounded actions — with evals, logging and transparency built in.

    KPI · Handle time · First-time-right · Escalation rate

How a deployment runs

Five stages. Production is the finish line.

Prototype doesn’t count. Production does.

  1. 01 · Exploration

    Exploration Days

    1–5 days · free

    Pick the workflow, capture the baseline, a working slice where access allows. You leave with a priced Lab proposal.

  2. 02 · Lab

    Workflow Lab

    3 weeks · credited against the Sprint

    A working prototype on real data, in your tenant, with the domain owner in the room.

  3. 03 · Sprint

    Production Sprint

    8–10 weeks · fixed fee

    Integration, evals, approval UI, audit logs, monitoring, handover docs. Adoption certified by the domain owner.

  4. 04 · Expansion

    Expansion Pod

    6–12 months

    The pod on the next adjacent workflows, priced on fee plus outcome.

  5. 05 · Handover

    Handover

    Your team runs it

    Code in your repos, documentation in your hands, your engineers extending what we built.

Architecture

The architecture every deployment shares.

Your systems of record stay where they are. Data is governed in Unity Catalog or Fabric’s equivalent. The model is chosen by eval, not by partnership. Every action an agent takes passes a human approval step and is logged.

  1. Systems of recordSAP · Salesforce · WMS · ticketing · documents
  2. Lakehouse + Unity CatalogGoverned tables, lineage, access control
  3. Business semanticsEntities and rules as the workflow names them
  4. Agents + modelsClaude, OpenAI or open-weight — by eval
  5. Workflow appProposes; a person approves; everything logged
  6. The people who run itEmbedded in the tool they already use
  7. A measured outcomeKPI delta against the Exploration baseline

Why IntegraBricks

True, and checkable.

  • Multi-cloud

    The same pod ships on Azure, AWS and GCP.

  • Model-neutral

    Claude, OpenAI or open-weight — chosen by eval, not by partnership.

  • Production is the finish line

    A Lab is not the product. A running workflow with a measured KPI is.

  • Code in your repos

    The workflow's IP is yours. Your team runs it after handover.

  • Evals and audit logs from day one

    EU AI Act transparency duties apply now; high-risk duties from December 2027.

  • Based in Amsterdam

    On site with your team across the Netherlands; the Nordics by arrangement.

Where we’ve worked

Inside financial services, insurance, leasing and mobility groups in the Netherlands and Germany — from Amsterdam.

Writing

One technical post per deployment.

The architecture, the evals, the before-and-after KPI.

[First post follows the first Production Sprint]

Exploration Days

Bring one workflow. We’ll spend one to five days on it.

Free, on site, and gated: a named workflow, a domain owner who attends, and data access that week. You leave with a baseline, a working slice where access allows, and a priced Lab proposal.

Book Exploration Days

Three questions before we book

  1. The workflow
  2. The domain owner
  3. The data platform